Download SuperTarget goes quantitative: update on drug–target interactions

Published online 8 November 2011
Nucleic Acids Research, 2012, Vol. 40, Database issue D1113–D1117
doi:10.1093/nar/gkr912
SuperTarget goes quantitative: update on
drug–target interactions
Nikolai Hecker1, Jessica Ahmed1, Joachim von Eichborn1, Mathias Dunkel1,
Karel Macha1, Andreas Eckert1, Michael K. Gilson2, Philip E. Bourne2 and
Robert Preissner1,*
1
Structural Bioinformatics Group, Institute for Physiology, Charité-University Medicine Berlin, Lindenberger Weg
80, 13125 Berlin, Germany and 2Skaggs School of Pharmacy and Pharmaceutical Sciences, University of
California San Diego, 9500 Gilman Drive, La Jolla CA 92093, USA
Received September 15, 2011; Revised October 6, 2011; Accepted October 8, 2011
ABSTRACT
There are at least two good reasons for the ongoing interest in drug–target interactions: first,
drug-effects can only be fully understood by considering a complex network of interactions to multiple targets (so-called off-target effects) including
metabolic and signaling pathways; second, it is
crucial to consider drug-target-pathway relations
for the identification of novel targets for drug
development. To address this on-going need, we
have developed a web-based data warehouse
named SuperTarget, which integrates drug-related
information associated with medical indications,
adverse drug effects, drug metabolism, pathways
and Gene Ontology (GO) terms for target proteins.
At present, the updated database contains >6000
target proteins, which are annotated with >330 000
relations to 196 000 compounds (including approved
drugs); the vast majority of interactions include
binding affinities and pointers to the respective literature sources. The user interface provides tools
for drug screening and target similarity inclusion.
A query interface enables the user to pose complex queries, for example, to find drugs that target
a certain pathway, interacting drugs that are
metabolized by the same cytochrome P450 or
drugs that target proteins within a certain affinity
range. SuperTarget is available at http://bioinformatics.charite.de/supertarget.
INTRODUCTION
In the last decade, non-commercial drug- or target-related
databases have been established. Millions of compounds
can be found in databases like ChEMBL (1) or PubChem
(2) and their availability can be checked via databases like
ZINC (3).
Several databases collect binding data on small molecules, in particular, drugs. A comprehensive and manually
curated resource is DrugBank (4), which contains 4300
targets related to about 7000 compounds, including 1500
FDA-approved drugs. Another notable database is the
Therapeutic Target Database (TTD) (5), which holds target information on approximately 2000 classified targets
linked to 5000 compounds, including 1500 approved
drugs. Surprisingly, the overlap between these two databases is small (data not shown). KEGG DRUG is a
database for approved drugs, which comprises drug–
target interactions, drug classifications as well as information about drug structure development (6). Other
databases collect drug–target data with a special focus
regarding medical indications [e.g. cancer (7) and infection
(8)], technical aspects [e.g. pharmacophores (9) or scaffold
hoppers (10)], side effects (11) or special metabolic
pathways (12). The database STITCH is focused on the
relation of >70 000 chemicals to targets from hundreds
of different organisms (13). To understand the complex
effects of drugs, the relation of their targets in signaling and metabolic pathways are important and reflected in
a number of databases, e.g. KEGG (6) or Reactome (14).
In 2008, the first version of SuperTarget was developed
with the intention to accentuate drug–target interactions
themselves and to provide references to other resources for
more elaborate analysis (15). Adverse drug reactions are a
common reason for the rejection of drug candidates during
clinical trial or withdrawal after approval. For example,
the cyclo-oxygenase inhibitor rofecoxib (nonsteroidal
anti-inflammatory drug) was withdrawn worldwide because of severe cardiovascular side effects, which may be
caused by unanticipated interactions with potassium and
calcium channels (16).
The analysis of drug–target interactions can play a
crucial role to improve the process of drug design and
*To whom correspondence should be addressed. Tel: +49 30 450540755; Fax: +49 30 450540955; Email: [email protected]
ß The Author(s) 2011. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
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D1114 Nucleic Acids Research, 2012, Vol. 40, Database issue
admission. SuperTarget provided a variety of drug–target
interactions and affected biological pathways in a
user-friendly manner. This second release of SuperTarget
contains a core dataset of 330 000 drug–target interactions, of which about 310 000 interactions have binding
affinity data. We consider a drug–target relation as a
specific interaction of a small chemical compound, which
could be used to treat or diagnose a disease. Thus,
SuperTarget now enables scientists to carry out not only
qualitative but also quantitative analysis of drug–target
interactions.
DATA SET
SuperTarget at present contains an updated version of
the original dataset. In 2011, the core dataset consists of
6219 targets and 195 770 drugs and putative drugs of
which about 2500 are approved drugs, that are classified
by the World Health Organization (WHO), resulting in
332 828 drug–target interactions. The list of targets was
selected using the PROMISCUOUS database (17). New
drug interactions were added from inhouse text mining,
supplemented by manual curations, SuperSite (18), CaRe
(7), SuperCyp (12) and DrugBank (4) using the target list
mentioned above. Target synonyms and external database
identifiers were updated as defined in the UniProtKB
database (19). The information content of drug entities
was enlarged by general properties such as molecular
weight, lipophilicity (logP) and known side effects as
defined by the Sider database (11). Binding affinities of
drug–target interactions were added from BindingDB
entries (20). SuperTarget offers references to various resources, which provide more detailed information, i.e.
specific links to PubChem, UniProtKB, DrugBank, the
RCSB Protein Data Bank (PDB), PubMed and the
BindingDB.
Relations from other databases, namely DrugBank (4),
KEGG (6), PDB(21), SuperDrug (22) and TTD (5) were
checked for drug–target interactions not identified using
the preceding steps. If those interactions could be confirmed by literature listed in PubMed, the references were
included in SuperTarget otherwise the describing database
is referenced. To provide users with further information
on drug–target interactions, SuperTarget provides links to
physicochemical properties and further structural information of drugs. Proven or potential target proteins are
represented as stored in UniProtKB (19), by functional
annotations extracted from GO (23), and by related
pathway information provided by KEGG (6) (compare
Figure 1).
FEATURES
SuperTarget enables users to link drugs and targets to
biomolecular pathways. Pathways are given as defined in
the KEGG database. Furthermore, targets can be searched
using gene ontology (GO) terms. The Anatomical
Therapeutical Chemical (ATC) classification of drugs
(24) is useful for searching drugs in distinct indication
areas and for analyzing co-occurrence of drugs/targets in
different diseases, which could be combined with side
effect searches. A special section is dedicated to drug interactions with enzymes of the Cytochrome P450 family
(CYP). CYPs are mono-oxygenases whose functions in
humans include the detoxification of foreign substances
via chemical modification. Hence, they play a crucial
role in drug metabolism. The features described above
were part of the first release, but have now been updated
extensively. Among other new features, SuperTarget
now introduces integration of protein–protein interaction
data from the ConsensusPathDB (25). In addition, target
sequence and drug similarities are incorporated in
SuperTarget. Drug similarities are computed using
Tanimoto coefficients of pre-calculated fingerprints as
implemented in SuperPred (26).
WEB INTERFACE
The accessibility and presentation of data is as important
as its accumulation and integration. The SuperTarget web
interface offers a variety of ways to obtain and view information about the drugs and targets:
(i) To provide quick access to the data, a simple fulltext search called ‘Targle’ was implemented, which
returns hits separately for drugs, targets and
pathways.
(ii) There is also a dedicated search section for each
type of entity: i.e. drugs, targets, pathways, gene
ontologies and CYPs. The user is either able to
select predefined identifiers or to type in a variety of
search terms. For instance, targets can be searched
by synonyms, UniProtKB identifiers, PDB identifiers, KEGG target identifiers or EC numbers. The
results section for each entity provides detailed information about each instance, which includes
SMILES and InChI strings and a list of putative
targets for drug results as well as a list of protein–
protein interactions and similar targets for target
results. All different entities are cross-linked, thus
details of putative targets and affected pathways
are easily accessible from drug search results.
(iii) For more sophisticated searches, an advanced search
option is available, which includes general properties, e.g. a desired number of H-bond donors, or
characteristics associated with particular drugs or
targets such as affinity values. This option allows
an arbitrary combination of different search criteria.
Hence, the user is able to perform a variety of
complex searches.
CASE STUDY: VASCULAR ENDOTHELIAL
GROWTH FACTOR RECEPTORS
The following case study illustrates a useful application of
the advanced search capability. Pathways, which include
Vascular Endothelial Growth Factors (VEGF), play an
important role in angiogenesis and provide targets for
anti-angiogenic cancer therapy (27). It has been shown
that normalization of tumor vasculature via inhibition of
Nucleic Acids Research, 2012, Vol. 40, Database issue
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Figure 1. System architecture and number of database entries of SuperTarget. The three clouds represent drugs (red), targets (yellow) and pathways
(blue) with given numbers, while the numbers of the respective relations are given at the connecting arrows. Beside the targets, drugs and pathways
the database provides >6500 GO-terms associated with drug–target interaction and 30 000 links to protein structures are given. Furthermore,
protein–protein interactions from the ConsensusPathDB are included.
VEGFR-2 receptor reduces hypoxia and cell proliferation
and increases accessibility to other chemotherapeutic
drugs (28). Therefore, it may be desirable to find specific
inhibitors of VEGFR-2, which do not affect the other
receptor subtype VEGFR-1. A general idea about the specificity of an inhibitor is provided by its IC50 value. This
refers to the compound concentration, which is needed
for a half-maximum inhibition of the corresponding
biological process. Thus, potent inhibitors show low
IC50 values whereas weak inhibitors exhibit relatively
high ones. The IC50 values for the two different
VEGFR subtypes should differ by at least an order of
magnitude (e.g. nM versus mM). Compounds with the
desired properties can be searched in SuperTarget as
follows:
First, both receptor subtypes are searched by their
UniProt name in the target section and are added to the
basket. Second, the advanced search section is used to
identify potential drugs that inhibit VEGFR-2 but not
VEGFR-1. In more detail, the VEGFR-1 receptor subtype
is added from the basket to the query as an ‘is ligand of
(‘‘VGFR1_HUMAN’’)’ search criterion. A range of IC50
values is defined as a second search criterion (1001–
100 000 nM for VEGFR-1). This criterion is automatically
associated with the first one. The steps mentioned above
are then repeated for the second receptor subtype, this
time the desired range for the IC50 value is 0–100 nM.
This query returns nine candidates. Since different experiments may suggest different IC50 values for the same
biological process, the results have to be manually
curated. The corresponding information is shown in the
interaction detail sections of the compounds and
VEGFR-1 or VEGFR-2, which are accessible from the
drug details page of each compound. The results of our
analysis suggest that at least six putative drugs with this
feature may be worth further investigation, i.e.
ChEMBL205413, ChEMBL205610, ChEMBL209919,
ChEMBL213507, ChEMBL380397 and ChEMBL398610
(Figure 2).
In two following steps, the crude cell biological context
of these six drugs can be analyzed. The six putative drugs
are added to the basket. In the next step, additional targets
of the six putative drugs can be identified. Similarly to the
steps mentioned above, for each of the six putative drugs
the advanced search option is used to search targets, which
are strongly inhibited by the compound, i.e. have IC50
values between 0 and 100 nM. Each additional target is
added to the basket. Three of the putative drugs,
ChEMBL209919, ChEMBL213507 and ChEMBL380397,
also show a strong inhibitory effect on the mast/stem
cell-growth factor receptor (UniProt name: FLT3_
HUMAN). ChEMBL398610 exhibits low IC50 values
toward the FL cytokine receptor (UniProt name:
FLT3_HUMAN), the high affinity nerve growth factor
receptor (UniProt name: NTRK1_HUMAN) and
VEGFR-3 (UniProt name: VGFR3_HUMAN).
In a final step, the list of additional targets can be used
to identify pathways, which are affected by the six putative
drugs mentioned in the first paragraph of this case study.
Either the advanced search option or the pathway link in
the list of results can be used to identify pathways containing VEGFR-2. VEGFR-2 is associated with the
human cytokine–cytokine interaction, endocytosis, focal
adhesion and the VEGF signaling pathway. All of the
six putative drugs are likely to have an impact on these
pathways. In a similar fashion, additional pathways
comprising the mast/stem cell growth factor receptor,
which is a target of ChEMBL209919, ChEMBL213507
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Figure 2. Case study: query and results. (1) Target search results for VEGFR-1 and VEGFR-2 are added to the basket. (2) Search criteria are
defined: select all drugs, which showed a low VEGFR-1 binding affinity (IC50 value between 1001 and 100 000) but a high VEGFR-2 affinity (IC50
value between 0 and 100 nM). (3) Result pages for each drug–target relation show detailed information on drug, target and binding affinity and (4)
An examination of the query results identifies six compounds with the desired properties. Refer to the text for further information.
and ChEMBL380397, are the human hemapoietic cell
lineage, melagonesis, acute myeloid leukemia pathway
and pathways in cancer. For the compound
ChEMBL398610, the union of pathways, which contain
at least one of its targets, appears to be interesting, i.e.
pathways comprising the FL cytokine receptor or the
high-affinity nerve growth factor receptor or VEGFR-3.
Hence, these three targets are added as pathway search
criteria in the advanced search section and combined by
OR conjunctions. In addition to the pathways associated
with VEGFR-2, the results include the Human MAPK
signaling, neurotrophin signaling, hemapoietic cell
lineage, apoptosis, acute myeloid leukemia, thyroid
cancer pathway and pathways in cancer.
CONCLUSION
SuperTarget is one of the largest resources of validated
drug–target interactions including quantitative data. We
carefully assessed the most relevant data sections for each
entity and provide links to many other resources. The
updated search engine allows users to obtain information
starting from a variety of entry points and to perform
complex queries.
AVAILABILITY
SuperTarget is available via the web site without registration: http://bioinformatics.charite.de/supertarget. It can
be used under the terms of the Creative Commons
Attribution-Noncommercial-Share Alike 3.0 License.
ACKNOWLEDGEMENTS
The authors wish to thank Helen Taubman for
proofreading.
FUNDING
BMBF (MedSys 0315450A), DFG (RTG ‘Computational
Systems Biology’ GRK1772 and IRTG ‘Systems Biology
of Molecular Networks’ GRK1360), EU (SynSys) and
NIH (GM070064). Funding for open access charge:
DFG GRK1772.
Conflict of interest statement. None declared.
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